Which ensemble method adjusts weights for misclassified instances in iterative training?
- Bagging
- Gradient Boosting
- Random Forest
- K-Means Clustering
Gradient Boosting is an ensemble method that adjusts weights for misclassified instances in iterative training. It aims to correct the errors made by the previous models in the ensemble, with a focus on improving prediction accuracy. This method is particularly effective in building strong predictive models by iteratively focusing on the data points that are challenging to classify correctly.
Loading...
Related Quiz
- In an ETL pipeline, which component is primarily responsible for transforming the data into a suitable format or structure for querying and analysis?
- Which statistical test is used to determine if there's a significant difference between the means of two independent groups?
- In which type of data do you often encounter a mix of structured tables and unstructured text?
- In L2 regularization, the penalty is proportional to the _______ of the magnitude of the coefficients.
- Apache Spark's core data structure, used for distributed data processing, is called what?